{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "% matplotlib inline\n",
    "\n",
    "import matplotlib.style\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = 12, 4\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pydetect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10df5b208>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NGgGWShQGtmWGly9fjo9//OPmTqqsljpHrYDWAOSegaIZDDeDjuy3NObJBgWD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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df429b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.randn(100)\n",
    "data[40] = 5.\n",
    "s = pd.Series(data)\n",
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.0\n",
       "1     0.0\n",
       "2     0.0\n",
       "3     0.0\n",
       "     ... \n",
       "96    0.0\n",
       "97    0.0\n",
       "98    0.0\n",
       "99    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detector = pydetect.GESDDetector()\n",
    "anm = detector.detect(s)\n",
    "anm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10df94198>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1038b5c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anm.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110993e48>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hUzsoizhLxk4KjTrh8lJAFbCh20NQ1gkXHQSSku151qixwNEiaGWFPc9zQQ8W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\nLCIiIiJh6f8AYvJ3DjGS2acAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11099c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ap = pydetect.datasets.get_airpassengers()\n",
    "ap[50] += 100\n",
    "ap.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1949-01-01    0.0\n",
       "1949-02-01    0.0\n",
       "1949-03-01    0.0\n",
       "1949-04-01    0.0\n",
       "             ... \n",
       "1960-09-01    0.0\n",
       "1960-10-01    0.0\n",
       "1960-11-01    0.0\n",
       "1960-12-01    0.0\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if input has trend and seasonality, use decompose=True\n",
    "detector = pydetect.GESDDetector(decompose=True)\n",
    "anm = detector.detect(ap)\n",
    "anm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111c89978>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110df2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anm.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
